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Muhammad Imran

Publications and source records attributed to Muhammad Imran.

At least 19 recordsLinked to original sources
arXiv2026

Hidden parabolic subgroups of $\mathrm{GL}_n$ over finite chain rings

We give a bounded-error quantum polynomial-time algorithm for finding hidden parabolic subgroups of $\mathrm{GL}_n(R)$ over any effectively presented finite chain ring $R$. We call auxiliary group elements or subgroups used to extract information through oracle queries \emph{probes}. For chain length at least two, elementary abelian socle probes first recover the complete flag modulo the maximal ideal. When the residue field has size $q\ge3$, we reconstruct the flag recursively, lifting rank-one terminal members one digit at a time. The intermediate digits reduce to abelian hidden subgroup problems, while the final digit is a finite-field multiple-shift problem solved using learning from subset and Fourier sampling. For $q=2$, where distinct flags can have the same stabilizer, we instead lift the conjugate parabolic subgroup through the congruence filtration. A final linear system determines it modulo normalizer directions. The algorithm outputs generators with probability at least $1-ε$ in time $\mathrm{poly}(n,\log|R|,\log(1/ε))$. Consequences include $\mathrm{GL}_n(\mathbb{Z}/N\mathbb{Z})$ for every $N\ge2$ and, with an explicit maximal-order presentation, $\mathrm{GL}_n(\mathcal{O}_K/N\mathcal{O}_K)$ for number fields $K$.

quant-ph↗
arXiv2026

Exact quantum splitting and the structure of finite algebras

For squarefree $f\in\F_q[x]$, Berlekamp's fixed-point algebra is $B\cong\F_q^r$, where $r$ is the number of irreducible factors. We give an exact quantum splitting procedure in a circuit model permitting efficiently computable, input-dependent single-qubit rotations. For a block with $s\ge2$ factors, a quadratic-character test (odd $q$) or absolute-trace test (characteristic $2$) is nonconstant with an exactly known probability $p_{q,s}\ge1/2$. Its value depends only on $q,s$, not on the hidden factors. An auxiliary-qubit reduction to success probability $1/4$ makes one Grover iteration exact. Thus factorization takes exactly $r-1$ splitting rounds and $O(n^3\log q)$ quantum $\F_q$-operations, with deterministic polynomial classical preprocessing and bookkeeping. The method also extracts primitive idempotents of arbitrary finite-dimensional commutative unital algebras given by structure constants in $O(n^4\log q)$ quantum $\F_q$-operations, staying over $\F_q$ throughout. Computing the radical and passing to the center extends it to the decomposition of $A/\Rad(A)$ into simple two-sided ideals, for an arbitrary finite-dimensional associative $\F_q$-algebra $A$.

quant-ph↗
arXiv2026

AI-Driven Disaster Response and Displacement Monitoring

ARTIFICIAL INTELLIGENCE (AI) has demonstrated its potential across a wide range of humanitarian tasks, offering scalable and data-driven solutions to some of the world's most pressing challenges. For instance, in addressing climate change, AI-powered models are being used for faster and more accurate global weather forecasting, control and design of renewable energy, and monitoring deforestation. Machine learning algorithms analyze satellite imagery and socioeconomic data to predict poverty and promote sustainable development. AI has also transformed disaster response and conflict monitoring, providing innovative solutions for assessing damage and urgent needs and tracking displacement.

cs.AI↗
arXiv2026

Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing

This study addresses the problem of structured sentiment analysis, whose goal is to obtain a fine-grained sentiment graph where the nodes represent spans of sentiment holders, targets, and expressions, while the arcs define the relationships among them. Our proposed approach casts the task as dependency graph parsing, but departs from traditional parsing methods by solving it through sequence labeling. To do so, we leverage recent advances in linearized graph encodings that allow each word in the input to be assigned a label, effectively capturing the structure of the dependency graph. We conducted experiments on seven datasets spanning five languages (English, Spanish, Norwegian, Basque, and Catalan), showing performance competitive with leading, more complex single-model approaches.

cs.CL↗
arXiv2026

Chiral symmetry breaking for large "Nf" and the properties of the "rho" meson near the conformal window

We study how the ground-state properties of the rho meson respond when the number of light-quark flavors N_f is driven towards the conformal window. The interaction is a symmetry-preserving, confining vector--vector contact interaction whose effective coupling carries a flavor dependence designed to encode the screening produced by fermion loops in the deep infrared. That interaction feeds the Schwinger--Dyson equation for the dressed-quark propagator and the homogeneous Bethe--Salpeter equation in the rainbow-ladder truncation, regularized in the Schwinger proper-time scheme so that quark production thresholds are absent while chiral symmetry remains broken. Raising N_f weakens the coupling, suppresses the dynamically generated mass and restores chiral symmetry together with deconfinement at a critical flavor number N^{c}_{f}. The vector channel responds differently from the pseudoscalar one. Because the $ρ$ is not a Goldstone boson, its mass is not protected by the symmetry that is being restored, and it simply follows the shrinking constituent scale, while the canonically normalized amplitude E_rho and the leptonic decay constant f_rho soften along the way. In this framework the rho sits above the nominal q\bar{q} threshold for every flavor number examined, so the state survives only as long as confinement removes that threshold. Dissociation is therefore governed by the divergence of the confinement length scale rather than by a crossing of the bound-state mass, and we contrast both criteria. The three elastic electromagnetic form factors G_E, G_M and G_Q, computed in the generalized impulse approximation, flatten as N_f grows, the zero crossing of G_E migrates towards the infrared and the charge radius expands, whereas the magnitude of the quadrupole moment decreases. The rho thus becomes a larger and rounder object as the theory approaches conformality.

hep-ph↗
arXiv2026

Filtering-out poor-quality images for data preparation

Filtering noise is a fundamental part of data preparation that enhances image quality for applications such as object segmentation, detection, and recognition. Various noise reduction techniques are proposed in the literature, including the use of median, Gaussian, and bilateral filters. Convolutional neural networks (CNNs) have gained popularity in image denoising owing to their ability to extract complex patterns and features from data. CNNs are highly adaptable, making them effective tools for various image-denoising tasks. One drawback of CNN-based techniques is that they require an appropriate training dataset and all images to be resized. Another notable drawback of all these filtering techniques is that they work for certain types of environmental and camera noises. To bridge this research gap, in this paper, for the first time, instead of denoising, we propose an approach that filters out poor-quality images for various environmental and camera impacts. In our approach, quality is assessed using an image quality assessment metric and an optimum threshold is used to filter out poor-quality images. We also ensure that a sufficient number of images remain to develop the deep learning (DL) model. The results produced using real and simulated traffic and object recognition data demonstrate the performance supremacy of the proposed approach compared with the state-of-the-art approaches. The average recognition accuracy for our proposed approach is 93.8% for the traffic sign recognition dataset and 84.9% for the object recognition dataset. This indicates our model's potential for real-life applications such as autonomous vehicles.

cs.CV↗
arXiv2026

Static Metrics Are Insufficient: Predicting Java Method Energy Usage with Execution Time

The increasing energy demand of software systems is raising concerns about their environmental impact and associated costs. Reasoning on energy usage early in the development flow has the potential to significantly reduce the overall energy usage of a software system, as it allows developers to make informed design and refactoring decisions before inefficiencies propagate. However, assessing energy usage without repeated profiling and direct measurement is difficult, which limits early reasoning in practice. This study investigates the limits of method-level energy prediction in Java, examining whether static source code metrics complemented with method-level execution time can estimate the energy consumption of Java methods. We profile 2,786 Java methods to extract 33 static features and measure execution time and energy, then train and compare eleven regression models. Our findings show that static source code metrics alone yield poor predictive performance, with average R2 values close to zero. Incorporating execution time as a lightweight dynamic input significantly improves accuracy, raising R2 to as high as 0.46. Execution time, internal method calls, and cyclomatic complexity consistently emerge as the strongest predictors of energy consumption.

cs.SE↗
arXiv2026

ErgoGlide: A Wearable Trackball Device for Ergonomic Text Entry in Virtual Reality

In virtual reality, it is challenging to achieve satisfactory text entry speed/accuracy, ergonomics, usability, and learnability. To address this issue, we developed ErgoGlide, a novel lightweight and compact wearable device that facilitates text entry tasks in virtual environments. The proposed ErgoGlide can be regarded as a small trackball that is wearable on a user's finger like a ring. By using ErgoGlide with a hive-like virtual keyboard, the user can rotate the ball for key selections, making text entry intuitive and accurate. We conducted three user studies to evaluate ErgoGlide and found that key confirmation techniques have significant effects on text entry speed and the hive-like keyboard design significantly reduced thumb movements. Furthermore, ErgoGlide can significantly improve typing accuracy, ergonomics, and usability over previous text entry methods. Experimental results also indicated that the typing speed of ErgoGlide can be notably improved after training.

cs.HC↗
arXiv2026

DisasterVQA: A Visual Question Answering Benchmark Dataset for Disaster Scenes

Social media imagery provides a low-latency source of situational information during natural and human-induced disasters, enabling rapid damage assessment and response. While Visual Question Answering (VQA) has shown strong performance in general-purpose domains, its suitability for the complex and safety-critical reasoning required in disaster response remains unclear. We introduce DisasterVQA, a benchmark dataset designed for perception and reasoning in crisis contexts. DisasterVQA consists of 1,395 real-world images and 4,405 expert-curated question-answer pairs spanning diverse events such as floods, wildfires, and earthquakes. Grounded in humanitarian frameworks including FEMA ESF and OCHA MIRA, the dataset includes binary, multiple-choice, and open-ended questions covering situational awareness and operational decision-making tasks. We benchmark seven state-of-the-art vision-language models and find performance variability across question types, disaster categories, regions, and humanitarian tasks. Although models achieve high accuracy on binary questions, they struggle with fine-grained quantitative reasoning, object counting, and context-sensitive interpretation, particularly for underrepresented disaster scenarios. DisasterVQA provides a challenging and practical benchmark to guide the development of more robust and operationally meaningful vision-language models for disaster response. The dataset is publicly available at https://doi.org/10.5281/zenodo.18267769.

cs.CV↗
arXiv2026

Verification and Validation (V&V)-in-the-Loop for RISC-V Design: The Holistic Vision of BZL

The Barcelona Zetascale Lab (BZL) project aims to strengthening Europe's capacity in the design and manufacture of RISC-V based high-performance computing chips. In this context, we present a holistic pre-silicon verification and validation (V&V) methodology targeting highly robust RISC-V chip designs. This paper provides an overview of BZL's V&V approach, which integrates three complementary platforms: (1) a UVM-based verification environment to thoroughly validate RTL functionality; (2) an FPGA-based validation platform that enables system-level pre-silicon hardware-software RTL validation; and (3) a CI/CD flow that continuously automates build, deployment, and tests across these domains. By embedding these platforms into an industrial-grade V&V loop and exploiting large-scale CPU and FPGA hardware infrastructures, the BZL project enables continuous evolution of reliable hardware development and software integration. We believe that the BZL's V&V flow represents a robust and scalable foundation for ensuring the pre-silicon functional correctness and system level validation of RISC-V chip designs, and can serve as a key enabler for strategic initiatives in Europe, such as EPI and DARE, and beyond.

cs.AR↗
arXiv2026

Dark solitons in nonlinear Su-Schrieffer-Heeger lattices

The introduction of nonlinearities into lattices with topological band structures has led to the discovery of various types of solitons. The Su-Schrieffer-Heeger (SSH) lattice, as the most fundamental topological model, has been extended into the nonlinear regime. In particular, nonlinear edge states and bulk solitons exhibiting intensity humps against a zero background have been extensively studied in nonlinear SSH lattices. In this paper, we systematically investigate dark solitons in nonlinear SSH lattices. These dark solitons maintain a nonzero and constant background, featuring intensity dips either in the bulk of the lattice or at its edges, and residing spectrally in the semi-infinite gap or the middle finite gap. Regardless of the specific type of dark soliton, the intensity dip remains wellpreserved and is not affected by the band structure of the original linear lattice. Although the dark solitons we have identified are generally dynamically unstable across a broad range of parameters, several types exhibit linear stability when the intracell coupling is much larger than the intercell coupling. Our findings may provide valuable insights for the exploration of novel types of solitons in nonlinear topological lattices.

nlin.PS↗
arXiv2026

GeoResponder: Towards Building Geospatial LLMs for Time-Critical Disaster Response

LLMs excel at linguistic tasks but lack the inner geospatial capabilities needed for time-critical disaster response, where reasoning about road networks, coordinates, and access to essential infrastructure such as hospitals, shelters, and pharmacies is vital. We introduce GeoResponder, a framework that instills robust spatial reasoning through a scaffolded instruction-tuning curriculum. By stratifying geospatial learning into different cognitive layers, we anchor semantic knowledge to the continuous coordinate manifold and enforce the internalization of spatial axioms. Extensive evaluations across four topologically distinct cities and diverse tasks demonstrate that GeoResponder significantly outperforms both state-of-the-art foundation models and domain-specific baselines. These results suggest that LLMs can begin to internalize and generalize geospatial structures, pointing toward the future development of language models capable of supporting disaster response needs.

cs.CL↗
arXiv2026

Symmetry-breaking bifurcation of coupled topological edge states

We propose that the symmetry-breaking bifurcation of coupled topological edge states (CTESs) can be used as a general principle for achieving spontaneous symmetry breaking (SSB) in a nonlinear topological lattice. Using an optical resonator array composed of two Su-Schrieffer-Heeger (SSH) chains as an example, we find that as the nonlinearity strength increases, the symmetric CTESs undergo a supercritical bifurcation. Beyond the critical threshold, the originally stable symmetric state becomes unstable, leading to the formation of a pair of stable asymmetric states. Both sides of the symmetric CTESs exhibit sublattice polarization, while the side of the asymmetric CTESs that is predominantly occupied demonstrates stronger sublattice polarization. We further find that as interchain coupling increases, the frequency range for stable CTESs expands, while the frequency range for stable asymmetric CTESs decreases. Our work provides a universal mechanism for realizing SSB in nonlinear topological lattices.

physics.optics↗
arXiv2026

Architecting Trust: A Framework for Secure IoT Systems Through Trusted Execution and Semantic Middleware

The Internet of Things (IoT) security landscape requires the architectural solutions that can address the technical and operational challenges across the heterogeneous environments. The IoT systems operate in different conditions, and security issues continue to increase. This paper presents the comprehensive security framework for IoT that should integrate the Trusted Execution Environments (TEEs) with the semantic middleware and blockchain technologies. The work provides a systematic analysis of the architectural patterns based on more than twenty recent research works and the existing standards, and it proposes a layered security architecture. The architecture includes the hardware rooted trust at peripheral level, the zero trust principles at network level, and the semantic security mechanisms at application level. The framework focuses on practical implementation aspects such as the performance overhead, interoperability requirements, and the compliance with new regulations, which are very important for the real IoT deployments. The paper reports quantitative metrics which include the cryptographic performance on Cortex-M class microcontrollers with the detection accuracy rates and the energy consumption values. The proposed architecture shows that cross-layer security integration can provide defense in depth while it still satisfies the constraints of resource-limited IoT environments. The discussion highlights open challenges and the future research directions for the IoT security architectures that include the post-quantum migration, secure federated model exchange and the automated compliance verification.

cs.CR↗
arXiv2026

A Syntax-Injected Approach for Faster and More Accurate Sentiment Analysis

Sentiment Analysis (SA) is a crucial aspect of Natural Language Processing (NLP), focusing on identifying and interpreting subjective assessments in textual content. Syntactic parsing is useful in SA as it improves accuracy and provides explainability; however, it often becomes a computational bottleneck due to slow parsing algorithms. This article proposes a solution to this bottleneck by using a Sequence Labeling Syntactic Parser (SELSP) to integrate syntactic information into SA via a rule-based sentiment analysis pipeline. By reformulating dependency parsing as a sequence labeling task, we significantly improve the efficiency of syntax-based SA. SELSP is trained and evaluated on a ternary polarity classification task, demonstrating greater speed and accuracy compared to conventional parsers like Stanza and heuristic approaches such as Valence Aware Dictionary and sEntiment Reasoner (VADER). The combination of speed and accuracy makes SELSP especially attractive for sentiment analysis applications in both academic and industrial contexts. Moreover, we compare SELSP with Transformer-based models trained on a 5-label classification task. In addition, we evaluate multiple sentiment dictionaries with SELSP to determine which yields the best performance in polarity prediction. The results show that dictionaries accounting for polarity judgment variation outperform those that ignore it. Furthermore, we show that SELSP outperforms Transformer-based models in terms of speed for polarity prediction.

cs.CL↗
arXiv2026

MATEX: Multi-scale Attention and Text-guided Explainability of Medical Vision-Language Models

We introduce MATEX (Multi-scale Attention and Text-guided Explainability), a novel framework that advances interpretability in medical vision-language models by incorporating anatomically informed spatial reasoning. MATEX synergistically combines multi-layer attention rollout, text-guided spatial priors, and layer consistency analysis to produce precise, stable, and clinically meaningful gradient attribution maps. By addressing key limitations of prior methods, such as spatial imprecision, lack of anatomical grounding, and limited attention granularity, MATEX enables more faithful and interpretable model explanations. Evaluated on the MS-CXR dataset, MATEX outperforms the state-of-the-art M2IB approach in both spatial precision and alignment with expert-annotated findings. These results highlight MATEX's potential to enhance trust and transparency in radiological AI applications.

cs.CV↗
arXiv2026

Predicting When to Trust Vision-Language Models for Spatial Reasoning

Vision-Language Models (VLMs) demonstrate impressive capabilities across multimodal tasks, yet exhibit systematic spatial reasoning failures, achieving only 49% (CLIP) to 54% (BLIP-2) accuracy on basic directional relationships. For safe deployment in robotics and autonomous systems, we need to predict when to trust VLM spatial predictions rather than accepting all outputs. We propose a vision-based confidence estimation framework that validates VLM predictions through independent geometric verification using object detection. Unlike text-based approaches relying on self-assessment, our method fuses four signals via gradient boosting: geometric alignment between VLM claims and coordinates, spatial ambiguity from overlap, detection quality, and VLM internal uncertainty. We achieve 0.674 AUROC on BLIP-2 (34.0% improvement over text-based baselines) and 0.583 AUROC on CLIP (16.1% improvement), generalizing across generative and classification architectures. Our framework enables selective prediction: at 60% target accuracy, we achieve 61.9% coverage versus 27.6% baseline (2.2x improvement) on BLIP-2. Feature analysis reveals vision-based signals contribute 87.4% of model importance versus 12.7% from VLM confidence, validating that external geometric verification outperforms self-assessment. We demonstrate reliable scene graph construction where confidence-based pruning improves precision from 52.1% to 78.3% while retaining 68.2% of edges.

cs.CV↗
arXiv2025

Exogenous Metal Cations in the Synthesis of CsPbBr3 Nanocrystals and their Interplay with Tertiary Amines

Current syntheses of CsPbBr3 halide perovskite nanocrystals (NCs) rely on over-stoichiometric amounts of Pb2+ precursors, resulting in unreacted lead ions at the end of the process. In our synthesis scheme of CsPbBr3 NCs we replaced excess Pb2+ with different exogenous metal cations (M) and investigated their effect on the synthesis products. These cations can be divided into two groups: group 1 delivers monodisperse CsPbBr3 cubes capped with oleate species (as for the case when Pb2+ is used in excess) and with photoluminescence quantum yield (PLQY) as high as 90% with some cations (for example with M= In3+); group 2 yields irregularly shaped CsPbBr3 NCs with broad size distributions. In both cases, the addition of a tertiary ammonium cation (didodecylmethyl ammonium, DDMA+) during the synthesis, after the nucleation of the NCs, reshapes the NCs to monodisperse truncated cubes. Such NCs feature a mixed oleate/DDMA+ surface termination with PLQY values up to 90%. For group 1 cations, this happens only if the ammonium cation is directly added as a salt (DDMA-Br) while for group 2 cations this happens even if the corresponding tertiary amine (DDMA) is added, instead of DDMA-Br. This is attributed to the fact that only group 2 cations can facilitate the protonation of DDMA by the excess oleic acid present in the reaction environment. In all cases studied, the incorporation of M cations is marginal and the reshaping of the NCs is only transient: if the reactions are run for a long time the truncated cubes evolve to cubes.

cond-mat.mtrl-sci↗